This App Reads Your Email to Guess Apple’s Sales Secrets – WIRED

Slice

When Apple released its fiscal third-quarter results in July—its first earnings report since the Apple Watch launched in nine countries—the company stayed conspicuously silent about just how many units of its new timepiece it sold. “This isn’t a matter of not being transparent, but a matter of not giving our competition insight,” CEO Tim Cook said on a call with analysts after the earnings numbers were made public.

With Apple not saying, the public could only rely on anecdotes and analyst estimates for Watch sales—which varied from as few as 3 million units to as many as 6 million. To make matters worse, we know from past earnings reports that analyst estimates can be way off the mark, even for more mature categories like the iPhone.

But one company thinks it can do better than the rest. Slice Intelligence, a company that tracks the shopping habits of consumers by reading their email receipts, surprised the world when it unveiled detailed information on how well it believed the Apple Watch sold in the US on its first day (957,000 units). By extrapolating from its receipts data, Slice claims it can do better than quarterly sales predictions for any Apple product—it can estimate day-by-day, or even hour by hour. Ahead of today’s iPhone event, Slice released a new report describing, among other things, how demand is highest following an Apple launch event, but that it also sees that demand eventually peters out to about 5 percent of the levels at launch. For a company as secretive as Apple, the promise of such market transparency is tantalizing. The question is how good its model really is.

Slice’s primary product is an app that users can download and give permission to comb through their email to detect receipts. From these, it can tell what you’ve bought and when, classifying the different items by purchase category (clothes, books, health and beauty, et cetera) or store. In essence, it works a lot like TripIt, an app for organizing travel itineraries, or Mint.com, which looks at your credit card statements.

Slice only tracks data from e-commerce channels unless a shopper has opted to get an email receipt sent to a Slice-linked inbox. But with that wealth of information, the company claims it can accurately extrapolate shopping behaviors—including how many Apple Watches the company has sold. According to Slice, the data they collect from its more than 2.5 million users correlates “nearly perfectly” with public disclosures by e-commerce companies, the US Department of Commerce, and its clients’ internal data.

‘It’s hard to act on email’

Slice was founded in 2010, when CEO Harpinder Singh and some other colleagues he had met at the Stanford Graduate School of Business came together to brainstorm about possible ventures in the “email intelligence” space. “We were amazed by what we were seeing with regards to the growth of e-receipts,” Singh tells WIRED. “We felt there was more and more purchase data that was showing up in people’s inboxes, including our own.”

Eric Schmidt, the executive chairman of Alphabet who was once a teacher at Stanford, was apparently convinced. He was the first investor in the company, and took part in Slice’s successful Series A round of funding in 2010. In 2013, Slice closed a Series B round. And by 2014, the company was acquired by Japanese internet giant and e-commerce company Rakuten.

The app includes some nifty features, such as the ability to track packages through their phases of shipping and get alerts when there’s a price drop or a product recall. In those cases, Singh says Slice can even pre-write a message with the relevant order number and purchase details, so all the user has to do is review the note and hit send in order to get a reimbursement.

Singh says as simple as that sounds, it took a lot of work to make the slew of receipt data coherent. “Data is kind of like clay, and organizing the massive deluge of product information we have into a taxonomy was a massive data science problem,” he says, pointing out they needed to be able to tell when a single television set, whether it came from Best Buy or Walmart or Amazon, is the exact one that saw a price drop or was recalled for some reason. Singh says Slice is now parsing data from more than 750,000 merchants altogether.

In my own experience using the app, I saw only a few misclassifications. Slice thought an Elena Ferrante book I had pre-ordered a while ago, for instance, belonged to “Movies and TV,” and it classified most of my ride-hailing receipts from Lyft under “Software and Mobile Apps,” while it knew my Uber rides should stay under “Travel.” But speaking from personal experience, the app seemed as good—and as imperfect—at choosing buckets as Mint.com’s money-managing app.

Mashing data sets

Perhaps the more interesting thing Slice can do with the data it gets, Singh says, is being able to mash the data with other existing data sets to glean new consumer insights. “For example, I could combine my data set with a merchant’s own data set in a blind, anonymous way, and see if a promotion resulted in an uplift [in sales],” Singh says. “Or I could combine my commerce data with geodata from companies that measure in-store foot traffic to derive correlations like, ‘Hey, Gap actually saw an increase in foot traffic from the coupon they sent out.’”

Slice can also take a company’s public disclosures, overlay its own data on top of that, and see how well its data statistically fits into the picture. And this also helps the company project e-commerce data into the future. “We are able to measure our user base against these public numbers,” Singh says.

But so far, at least as far as Apple is concerned, Slice hasn’t released much data to see how good its guesses really are. It’s put out loyalty and switching data around the launch of the iPhone 6 and 6 Plus, as well as for new iPad models. But it hasn’t publicly made any guesses that we could compare with numbers Apple has disclosed.

Still, Singh insists the Slice platform has the ability to offer unique insights that come from being able to collect information from the purchase habits of real people, and to do so passively, instead of relying on, for example, consumer surveys, which require active participation. “We like to say we collect data in the wild,” Singh says. “You don’t have to go to the zoo. We are seeing actions people take in their normal daily lives.”